Representation of j=1 rotation matrix

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Discussion Overview

The discussion revolves around the representation of the rotation matrix for j=1, specifically focusing on the mathematical derivation and properties of the operator J_y. Participants explore the implications of a specific equation involving J_y and its eigenvalues.

Discussion Character

  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant asserts that the equation [atex]\frac{J_y}{\hbar} = (J_y/\hbar)^3[/itex] is relevant for j=1, seeking a simpler derivation method.
  • Another participant challenges this assertion, stating that the equation is not generally true and points out the eigenvalues of J_y for j=1 are +1, 0, and -1, suggesting a potential special circumstance in the original claim.
  • A third participant agrees with the first claim but suggests that using matrix multiplication to compute <j'=1,m'| J_y|j=1,m> could simplify the proof.
  • A later reply reflects on a misunderstanding regarding the implications of a mathematical operation, indicating a realization of an error in reasoning.
  • One participant inquires about the abbreviation "evs," indicating a need for clarification on terminology used in the discussion.

Areas of Agreement / Disagreement

Participants express disagreement regarding the validity of the equation involving J_y, with some supporting its relevance for j=1 while others contest its general applicability. The discussion remains unresolved as differing viewpoints persist.

Contextual Notes

There are indications of missing assumptions regarding the conditions under which the equation may hold true, as well as potential dependencies on specific definitions or contexts related to the rotation matrix representation.

jdstokes
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[SOLVED] Representation of j=1 rotation matrix

The derivation of this involves the use of the following fact for j=1:

[atex]\frac{J_y}{\hbar} = (J_y/\hbar)^3[/itex].

Is there a simple way to see this other than slogging through the algebra by expanding out the RHS using J_y = \frac{1}{2i}(J_+ - J_i) and J_{\pm}|jm\rangle = \hbar\sqrt{(j\mp m)(j \pm m + 1)}| j,m\pm 1\rangle?
 
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jdstokes said:
The derivation of this involves the use of the following fact for j=1:
[atex]\frac{J_y}{\hbar} = (J_y/\hbar)^3[/itex].
That's not true in general, since the eigenvalues of J_y for j=1 are +1,0,-1.
It is not true for j=-1. Perhaps there is a special circumstance in your problem.
 
Hi Pam,

It is true for j=1, and I was being stupid anyway, the trick is to use matrix multiplication.

Ie just write down \langle j&#039;=1,m&#039;| J_y|j=1,m\rangle and do the trivial matrix multiplication. Doh!
 
Of course, I made the silly mistake of thinking (-1)^3=+1.
Using the evs is a simpler proof than even trivial matrix math.
 
evs?
 

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